Data animation is a process for summarizing and displaying data using an animation comprising a sequence of frames. The underlying data may reside in a database and/or may be modeled using a plurality of dimensions. Each frame of the animation may, for example, correspond to a different period of time and may comprise one or more plots, graphs and/or charts. The plots, graphs and/or charts may be based on two or more data variables. Each axis of the plot, graph or chart may represent a different dimension or measure. In addition, the data region of the plot, graph or chart may expose additional measures and/or dimensions of the underlying data. For example, a scatter plot may utilize different colors to visualize an otherwise hidden dimension.
In analyzing data, users may search for recurring patterns (e.g., clustering and the like), trends, and correlations. The underlying data may be represented using one or more measures and one or more dimensions. As the amount of data increases, the potential for data patterns to be hidden may increase. For example, viewing a summary of sales information over a period of twenty years may obscure data patterns that would be apparent in viewing sales information over a period of one year. Similarly, as the number of dimensions increases, the data may become more difficult to visualize.
An example of a potentially hidden data pattern is a shift in buying behavior that slowly occurs over an extended time period, e.g., ten years. Cumulative sales data for the ten year period may suppress a data pattern showing strong sales in the early years and weak sales in the latter years. Visualizing the underlying data may expose the shift in buying behavior. By sequentially displaying a plurality of frames of an animation at a specified refresh rate, patterns and trends may be recognized. Due to the requirements of a particular frame refresh rate and the time required to generate each frame, however, it may be difficult to create a data animation in substantially real-time.